SoroushVahidi/scidocs huggingface dataset:
LOMAC: Low Water-Mark Integrity Protection for COTS Environments
(2000)

We hypothesizethat a form of kernel-residentaccesscontrol-based integrity protection can gain widespread acceptancein Commer cial Off-The-Shelf(COTS)environmentsprovidedthat it couplessomeusefulprotectionwith a high degreeof compatibilitywith existing software, configurations,andpractices.To testthis hypothesis, wehave developeda highly-compatiblefreeopen-sour ceprototype calledLOMAC, andreleasedit on theInternet.LOMAC is a dynamicallyloadableextensionfor COTSLinux kernels thatprovidesintegrity protectionbasedonLowWater-Mark accesscontrol. We presenta classificationof existing accesscontrol modelswith regard tocompatibility, concluding thatmodelssimilar to LowWater-Mark areespeciallywellsuitedto high-compatibilitysolutions.Wealsodescribeour practical strategiesfor dealingwith thepathological cases in the Low Water-Mark model’s behavior , which includea smallextensionof themodel,andanunusualapplicationof
Experience in System Design for Human-Robot Teaming in Urban Search and Rescue
(2012)

The paper describes experience with applying a user-centric design methodology in developing systems for human-robot teaming in Urban Search & Rescue. A human-robot team consists of several semi-autonomous robots (rovers/UGVs, microcopter/UAVs), several humans at an off-site command post (mission commander, UGV operators) and one on-site human (UAV operator). This system has been developed in close cooperation with several rescue organizations, and has been deployed in a real-life tunnel accident use case. The human-robot team jointly explores an accident site, communicating using a multi-modal team interface, and spoken dialogue. The paper describes the development of this complex socio-technical system per se, as well as recent experience in evaluating the performance of this system.
A method for Automatic Text Summarization using Consensus of Multiple Similarity Measures and Ranking Techniques
(2016)

In the era of information overload, text summarization can be defined as the process of extracting useful information from a large space of available content using traditional filtering methods. One of the major challenges in the domain of extraction based summarization is that a single statistical measure is not sufficient to produce efficient summaries which would be close to human-made ‘gold standard’, since each measure suffers from individual weaknesses. We deal with this problem by proposing a text summarization model that combines various statistical measures so that the pitfalls of an individual technique could be compensated by the strengths of others. Experimental results are presented to demonstrate the effectiveness of the proposed method using the TAC 2011 Multiling pilot dataset for English language and ROUGE summary evaluation tool.
Improving Document Clustering for Short Texts by Long Documents via a Dirichlet Multinomial Allocation Model
(2017)

Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness representation. To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts. In this paper, we propose a novel model, namely DDMAfs, which 1) improves the clustering performance of short texts by sharing structural knowledge of long documents to short texts; 2) automatically identifies the number of clusters; 3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge. Our experiments indicate that the DDMAfs model performs well on the synthetic dataset and real datasets. Comparisons between the DDMAfs model and state-of-the-art short text clustering approaches show that the DDMAfs model is effective.
CBSE as Novel Approach for IDS
(2014)

This paper gives a new approach called Component based software engineering for designing an intrusion detection system. This paper also tells that before this component based software approach, other traditional approaches were used for designing intrusion detection system. But due to various drawbacks we proposed this component based approach for intrusion detection system. A comparison table is also illustrated in this paper on the basis of class diagram designed using UML language in a simulated environment. This comparison table will illustrate main difference between the traditional and component based approach and also shows that which parameters make this approach a better approach. Keywords—Component based software engineering, intrusion detection system, Classes etc.
Overview on electrostatic discharge protection designs for mixed-voltage I/O interfaces: design concept and circuit implementations
(2006)

Electrostatic discharge (ESD) protection design for mixed-voltage I/O interfaces has been one of the key challenges of system-on-a-chip (SOC) implementation in nano-scale CMOS processes. The on-chip ESD protection circuit for mixed-voltage I/O interfaces should meet the gate-oxide reliability constraints and prevent the undesired leakage current paths. This paper presents an overview on the design concept and circuit implementations of the ESD protection designs for mixed-voltage I/O interfaces without using the additional thick gate-oxide process. The ESD design constraints in mixed-voltage I/O interfaces, the classification and analysis of ESD protection designs for mixed-voltage I/O interfaces, and the designs of high-voltage-tolerant power-rail ESD clamp circuit are presented and discussed.
Enterprise-wide Optimization : A New Frontier in Process Systems Engineering
(2005)

Enterprise-wide optimization (EWO) is a new emerging area that lies at the interface of chemical engineering and operations research, and has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the operations of supply, manufacturing and distribution activities of a company to reduce costs and inventories. A major focus in EWO is the optimal operation of manufacturing facilities, which often requires the use of nonlinear process models. Major operational items include planning, scheduling, realtime optimization and inventory control. One of the key features of EWO is integration of the information and the decision-making among the various functions that comprise the supply chain of the company. This can be achieved with modern IT tools, which together with the internet, have promoted e-commerce. However, as will be discussed, to fully realize the potential of transactional IT tools, the development of sophisticated deterministic and stochastic linear/nonlinear optimization models and algorithms (analytical IT tools) is needed to explore and analyze alternatives of the supply chain to yield overall optimum economic performance, as well as high levels of customer satisfaction. An additional challenge is the integrated and coordinated decision-making across the various functions in a company (purchasing, manufacturing, distribution, sales), across various geographically distributed organizations (vendors, facilities and markets), and across various levels of decision-making (strategic, tactical and operational). © 2005 American Institute of Chemical Engineers AIChE J, 51: 1846–1857, 2005
Forecasting stock market movement direction with support vector machine
(2005)

Support vector machine (SVM) is a very speci1c type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of 1nancial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classi1cation methods. Further, we propose a combining model by integrating SVM with the other classi1cation methods. The combining model performs best among all the forecasting methods. ? 2004 Elsevier Ltd. All rights reserved.
Classifying the Informative Behaviour of Emoji in Microblogs
(2018)

Emoji are pictographs commonly used in microblogs as emotion markers, but they can also represent a much wider range of concepts. Additionally, they may occur in different positions within a message (e.g. a tweet), appear in sequences or act as word substitute. Emoji must be considered necessary elements in the analysis and processing of user generated content, since they can either provide fundamental syntactic information, emphasize what is already expressed in the text, or carry meaning that cannot be inferred from the words alone. We collected and annotated a corpus of 2475 tweets pairs with the aim of analyzing and then classifying emoji use with respect to redundancy. The best classification model achieved an F-score of 0.7. In this paper we shortly present the corpus, and we describe the classification experiments, explain the predictive features adopted, discuss the problematic aspects of our approach and suggest future improvements.
Sustainable Food Supply Chains : The Role of Collaboration and Sustainable Relationships
(2013)

The life cycle approach is widely used in the analysis of sustainability. Its application to supply chains is necessary since the product flows, from processing of raw materials to the final customer, are considered. The role of the organizational aspects, expressed in terms of relationships between the supply chain agents, is little considered in the life cycle analysis approach. The aim of this paper is to extend the scope of the food chain life cycle analysis by adding the organizational dimension to the environmental, economic and social ones. Within this context, Collaboration and Sustainable Relationships concepts have been explored based on a literature survey. A theoretical framework, describing their role in assessing the organizational dimension in the life cycle analysis of the food supply chains, is defined. A hypothesis on their joint influence on the supply chains performances is formulated.
Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling
(2017)

In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks’ execution latency and the MD’s energy consumption by jointly optimizing the task allocation decision and the MD’s central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks’ execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD’s flexible CPU range can have an impact on the task allocation.
Robustness beyond shallowness: incremental deep parsing
(2002)

Robustness is a key issue for natural language processing in general and parsing in particular, and many approaches have been explored in the last decade for the design of robust parsing systems. Among those approaches is shallow or partial parsing, which produces minimal and incomplete syntactic structures, often in an incremental way. We argue that with a systematic incremental methodology one can go beyond shallow parsing to deeper language analysis, while preserving robustness. We describe a generic system based on such methodology and designed for building robust analyzers that tackle deeper linguistic phenomena than those traditionally handled by the now widespread shallow parsers. The rule formalism allows the recognition of n-ary linguistic relations between words or constituents on the basis of global or local structural, topological and/or lexical conditions. It offers the advantage of accepting various types of inputs, ranging from raw to chunked or constituent-marked texts, so for instance it can be used to process existing annotated corpora, or to perform a deeper analysis on the output of an existing shallow parser. It has been successfully used to build a deep functional dependency parser, as well as for the task of co-reference resolution, in a modular way.
From Single to Dual Robotic Therapy: A Review of the Development Process of iPAM
(2006)

Stroke is a common condition in the UK with over 30,000 people per annum are left with significant disability. When severe arm paresis is occurs, functional recovery of the affected arm is often poor. This can have a major impact on physical independence. A contributing factor to the poor recovery relates to lack of intensity of appropriate arm exercise practice. Through the use of robotic technology it may be possible to augment the physical therapist's efforts and maximize the impact of the limited physical therapy resource. At Leeds University a prototype single robot system has been developed that is capable of moving a patient's arm through a set of movements prescribed by a physiotherapist. This system is robust to changes in the biomechanical and physiological properties through a large range of patients. Further development of the system has led to a dual robot system that provides a greater level of control over upper-arm joint co-ordination while offering additional support to the shoulder complex. In order to test the systems safely, two models of the human arm have been produced: 1) a 6 degree of freedom computational model simulating how the robot system could provide therapy to a patient. 2) A mechanical model (Derryck), representing the biomechanical properties of the shoulder and elbow. The prototype single robot system is shown to be capable of administering trajectory following (for Derryck) for a large range of arm parameters
Categorize the Quality of Cotton Seeds Based on the Different Germination of the Cotton Using Machine Knowledge Approach
(2011)

A study was made in those region that expose major area of machine learning techniques which available in data mining. There are lots of Research work is analysized in agriculture field. It is a smart techniques is needed to find the resolution over the modern method. Cotton is an significant beneficial harvest and extensively traded commodity across the world. Its yield is insightful to weather, soil as well as organization practices. This study presents a concise idea of several of the broadly capitalized machine learning technique over agricultural domain. Among the algorithms like Navie bayes, j48 ,MLP,Randomforest,Random tree, I have founded J48 gives better result over the other algorithms using cotton seed quality accuracy is best.
Facial Expression Analysis under Partial Occlusion: A Survey
(2018)

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.
A six degrees of freedom haptic interface for laparoscopic training
(2016)

We present the novel kinematics, workspace characterization, functional prototype and impedance control of a six degrees of freedom haptic interface designed to train surgeons for laparoscopic procedures, through virtual reality simulations. The parallel kinematics of the device is constructed by connecting a 3RRP planar parallel mechanism to a linearly actuated modified delta mechanism with a connecting link. The configuration level forward and inverse kinematics of the device assume analytic solutions, while its workspace can be shaped to enable large end-effector translations and rotations, making it well-suited for laparoscopy operations. Furthermore, the haptic interface features a low apparent inertia with high structural stiffness, thanks to its parallel kinematics with grounded actuators. A model-based open-loop impedance controller with feed-forward gravity compensation has been implemented for the device and various virtual tissue/organ stiffness levels have been rendered.
Commercial DC-DC converters: A survey of isolated low-voltage very-high-current systems for transportation electrification
(2017)

As the energy demand from low-voltage (LV) DC sources on vehicles keeps increasing, the need for DC-DC converters capable of handling very high current on the LV side is growing. A group of original equipment manufacturers (OEM) have set performance targets for such converters for the years 2015 and 2020. This paper presents an extensive survey of DC-DC converters that are: a) galvanically isolated, b) able to scale 12/24/48 V up to several hundred volts, and c) rated to carry several hundred amperes of current on the LV side. More than eighty converters from thirty manufacturers are analyzed. Characteristics such as voltage ranges, current capacity, power rating, efficiency, operating temperature, cooling types, weight, power density, volume, specific power, compliant codes, and standards are collected and presented in a graphical form. The current insights and trends are gathered and compared with the OEM targets. The results can serve as a technology indicator of this kind of converters in 2015 for researchers and practicing engineers of transportation electrification and others alike.
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
(2017)

Batch Normalization is quite effective at accelerating and improving the training of deep models. However, its effectiveness diminishes when the training minibatches are small, or do not consist of independent samples. We hypothesize that this is due to the dependence of model layer inputs on all the examples in the minibatch, and different activations being produced between training and inference. We propose Batch Renormalization, a simple and effective extension to ensure that the training and inference models generate the same outputs that depend on individual examples rather than the entire minibatch. Models trained with Batch Renormalization perform substantially better than batchnorm when training with small or non-i.i.d. minibatches. At the same time, Batch Renormalization retains the benefits of batchnorm such as insensitivity to initialization and training efficiency.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
(2017)

Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the ‘Fréchet Inception Distance” (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
(2016)

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.
Deep Networks with Stochastic Depth
(2016)

Very deep convolutional networks with hundreds of layers have led to significant reductions in error on competitive benchmarks. Although the unmatched expressiveness of the many layers can be highly desirable at test time, training very deep networks comes with its own set of challenges. The gradients can vanish, the forward flow often diminishes, and the training time can be painfully slow. To address these problems, we propose stochastic depth, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time. We start with very deep networks but during training, for each mini-batch, randomly drop a subset of layers and bypass them with the identity function. This simple approach complements the recent success of residual networks. It reduces training time substantially and improves the test error significantly on almost all data sets that we used for evaluation. With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4.91% on CIFAR-10).